BibSLEIGH — machine learning tag

machine learning

machine learning

Tag #machine learning

394 papers:

CGO-2020-CowanMCBC #automation #generative #kernel: Automatic generation of high-performance quantized machine learning kernels (MC, TM, TC, JB, LC), pp. 305–316.
ECSA-2019-GalsterGG #perspective #quality #what: What Quality Attributes Can We Find in Product Backlogs? A Machine Learning Perspective (MG, FG, FG), pp. 88–96.
EDM-2019-HuangBS #collaboration #identification #using: Identifying Collaborative Learning States Using Unsupervised Machine Learning on Eye-Tracking, Physiological and Motion Sensor Data (KH, TB, BS).
EDM-2019-JayaramanGG #identification #student #using: Supporting Minority Student Success by using Machine Learning to Identify At-Risk Students (JDJ, SG, JG).
EDM-2019-NandaD #categorisation: Machine Learning Based Decision Support System for Categorizing MOOC Discussion Forum Posts (GN, KAD).
EDM-2019-Woodruff #architecture #education #interactive #predict #student: Predicting student academic outcomes in UK secondary phase education: an architecture for machine learning and user interaction (MW).
ICPC-2019-PecorelliPNL #detection #heuristic #smell: Comparing heuristic and machine learning approaches for metric-based code smell detection (FP, FP, DDN, ADL), pp. 93–104.
ICSME-2019-KallisSCP #classification: Ticket Tagger: Machine Learning Driven Issue Classification (RK, ADS, GC, SP), pp. 406–409.
MSR-2019-BangashSCWHA #case study #developer #ml #stack overflow #what: What do developers know about machine learning: a study of ML discussions on StackOverflow (AAB, HS, SAC, AWW, AH, KA0), pp. 260–264.
SANER-2019-PupoNENRB #data flow #information management #named: GUARDIAML: Machine Learning-Assisted Dynamic Information Flow Control (ALSP, JN, KE, AN, CDR, EGB), pp. 624–628.
SEFM-2019-Kawamoto #logic #specification #statistics #towards: Towards Logical Specification of Statistical Machine Learning (YK0), pp. 293–311.
CoG-2019-DiazPF #game studies #interactive: Interactive Machine Learning for More Expressive Game Interactions (CGD, PP, RF), pp. 1–2.
CoG-2019-JohansenPR #game studies #video: Video Game Description Language Environment for Unity Machine Learning Agents (MJ, MP, SR), pp. 1–8.
CIKM-2019-LuLW0 #database #modelling #similarity #string: Synergy of Database Techniques and Machine Learning Models for String Similarity Search and Join (JL, CL, JW, CL0), pp. 2975–2976.
CIKM-2019-VazirgiannisNS #graph #kernel: Machine Learning on Graphs with Kernels (MV, GN, GS), pp. 2983–2984.
ICML-2019-BansalLRSW #higher-order #logic #named #proving #theorem proving: HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving (KB, SML, MNR, CS, SW), pp. 454–463.
ICML-2019-GhorbaniZ: Data Shapley: Equitable Valuation of Data for Machine Learning (AG, JYZ), pp. 2242–2251.
ICML-2019-KleimanP #metric #modelling #multi #named #performance: AUCμ: A Performance Metric for Multi-Class Machine Learning Models (RK, DP), pp. 3439–3447.
ICML-2019-QiaoAZX #fault tolerance: Fault Tolerance in Iterative-Convergent Machine Learning (AQ, BA, BZ, EPX), pp. 5220–5230.
KDD-2019-AhmedABCCDDEFFG #ml: Machine Learning at Microsoft with ML.NET (ZA, SA, MB, RC, WSC, YD, XD, VE, SF, TF, AG, MH, SI, MI, NK, GK, PL, IM, SM, SM, GN, JO, GO, AP, JP, PR, MZS, MW, SZ, YZ), pp. 2448–2458.
KDD-2019-BernardiME #lessons learnt #modelling: 150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com (LB, TM, PE), pp. 1743–1751.
KDD-2019-BirdHKKM #challenge #lessons learnt: Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned (SB, BH, KK, EK, MM), pp. 3205–3206.
KDD-2019-Caruana #black box #modelling: Friends Don't Let Friends Deploy Black-Box Models: The Importance of Intelligibility in Machine Learning (RC), p. 3174.
KDD-2019-Chen0 #data mining #mining #optimisation #order #robust: Recent Progress in Zeroth Order Optimization and Its Applications to Adversarial Robustness in Data Mining and Machine Learning (PYC, SL0), pp. 3233–3234.
KDD-2019-DongR #integration: Data Integration and Machine Learning: A Natural Synergy (XLD, TR), pp. 3193–3194.
KDD-2019-FauvelMFFT #detection #towards: Towards Sustainable Dairy Management - A Machine Learning Enhanced Method for Estrus Detection (KF, VM, ÉF, PF, AT), pp. 3051–3059.
KDD-2019-Guestrin: 4 Perspectives in Human-Centered Machine Learning (CG), p. 3162.
KDD-2019-HuNYZ #collaboration #distributed #framework #named: FDML: A Collaborative Machine Learning Framework for Distributed Features (YH, DN, JY, SZ), pp. 2232–2240.
KDD-2019-HwangOCPM: Improving Subseasonal Forecasting in the Western U.S. with Machine Learning (JH, PO, JC, KP, LM), pp. 2325–2335.
KDD-2019-LiakhovichD: Preventing Rhino Poaching through Machine Learning (OL, GDC), p. 3177.
KDD-2019-NetoPPTBMO #approach #health #permutation: A Permutation Approach to Assess Confounding in Machine Learning Applications for Digital Health (ECN, AP, TMP, MT, BMB, LM, LO), pp. 54–64.
KDD-2019-SrivastavaHK #approach: Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning (MS, HH, AK), pp. 2459–2468.
KDD-2019-WangLYLLZ0 #approach #nondeterminism #quantifier: Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting (BW, JL0, ZY0, HL, TL, YZ0, GZ0), pp. 2087–2095.
MoDELS-2019-NguyenRRPI #approach #automation #classification #metamodelling #repository: Automated Classification of Metamodel Repositories: A Machine Learning Approach (PTN, JDR, DDR, AP, LI), pp. 272–282.
Onward-2019-Allamanis #modelling: The adverse effects of code duplication in machine learning models of code (MA), pp. 143–153.
PLDI-2019-GopinathGSS #compilation #modelling: Compiling KB-sized machine learning models to tiny IoT devices (SG, NG, VS, RS0), pp. 79–95.
PLDI-2019-IyerJPRR #synthesis: Synthesis and machine learning for heterogeneous extraction (ASI, MJ, SP, AR, SKR), pp. 301–315.
ASE-2019-Balasubramaniam #representation #towards #using: Towards Comprehensible Representation of Controllers using Machine Learning (GB), pp. 1283–1285.
ASE-2019-JiangLJ #how #recommendation: Machine Learning Based Recommendation of Method Names: How Far are We (LJ, HL, HJ), pp. 602–614.
ASE-2019-Zhang #approach #identification #injection #sql: A Machine Learning Based Approach to Identify SQL Injection Vulnerabilities (KZ), pp. 1286–1288.
ESEC-FSE-2019-AggarwalLNDS #black box #modelling #testing: Black box fairness testing of machine learning models (AA, PL, SN, KD, DS), pp. 625–635.
ESEC-FSE-2019-FucciMM #api #documentation #identification #on the #using: On using machine learning to identify knowledge in API reference documentation (DF, AM, WM), pp. 109–119.
ESEC-FSE-2019-Moghadam #performance #testing: Machine learning-assisted performance testing (MHM), pp. 1187–1189.
ESEC-FSE-2019-MostaeenSRRS #named #validation: CloneCognition: machine learning based code clone validation tool (GM, JS, BR, CKR, KAS), pp. 1105–1109.
ASPLOS-2019-AnkitHCNFWFHS0M #named #programmable: PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference (AA, IEH, SRC, GN, MF, RSW, PF, WmWH, JPS, KR0, DSM), pp. 715–731.
CASE-2019-ChenLVR #process #using: Strip Snap Analytics in Cold Rolling Process Using Machine Learning (ZC, YL, AVM, FR), pp. 368–373.
CASE-2019-KoWNL #information management: Machine Learning based Continuous Knowledge Engineering for Additive Manufacturing (HK, PW, NYN, YL), pp. 648–654.
CASE-2019-LauerL #predict: Plan instability prediction by machine learning in master production planning (TL, SL), pp. 703–708.
CASE-2019-MatsuokaNT #approach #identification #problem #scheduling: Machine Learning Approach for Identification of Objective Function in Production Scheduling Problems (YM, TN, KT), pp. 679–684.
CGO-2019-Castro-LopezL #compilation #deployment #modelling #multi: Multi-target Compiler for the Deployment of Machine Learning Models (OCL, IFVL), pp. 280–281.
ICST-2019-KahlesTHJ #agile #analysis #automation #testing: Automating Root Cause Analysis via Machine Learning in Agile Software Testing Environments (JK, JT, TH, AJ), pp. 379–390.
ICST-2019-KocWFCP #assessment #empirical #java #static analysis: An Empirical Assessment of Machine Learning Approaches for Triaging Reports of a Java Static Analysis Tool (UK, SW, JSF, MC, AAP), pp. 288–299.
ICST-2019-SharmaW #algorithm #testing: Testing Machine Learning Algorithms for Balanced Data Usage (AS, HW), pp. 125–135.
ICTSS-2019-AichernigB0HPRR #behaviour #hybrid #modelling #testing: Learning a Behavior Model of Hybrid Systems Through Combining Model-Based Testing and Machine Learning (BKA, RB, ME0, MH, FP, WR, AR, MT, MT), pp. 3–21.
ICTSS-2019-NakajimaC #dataset #generative #source code #testing: Generating Biased Dataset for Metamorphic Testing of Machine Learning Programs (SN0, TYC), pp. 56–64.
JCDL-2018-Nielsen #library: Introduction to Machine Learning for Digital Library Applications (RDN), pp. 421–422.
JCDL-2018-TkaczykCSB #evaluation #open source #parsing: Machine Learning vs. Rules and Out-of-the-Box vs. Retrained: An Evaluation of Open-Source Bibliographic Reference and Citation Parsers (DT, AC, PS, JB), pp. 99–108.
ICSME-2018-MillsEH #automation #classification #maintenance #traceability: Automatic Traceability Maintenance via Machine Learning Classification (CM, JEA, SH), pp. 369–380.
MSR-2018-BraiekKA08 #framework: The open-closed principle of modern machine learning frameworks (HBB, FK, BA), pp. 353–363.
MSR-2018-BulmerMD #developer #ide #predict: Predicting developers' IDE commands with machine learning (TB, LM, DED), pp. 82–85.
SANER-2018-NucciPTSL #detection #question #smell #using: Detecting code smells using machine learning techniques: Are we there yet? (DDN, FP, DAT, AS, ADL), pp. 612–621.
SCAM-2018-MostaeenSRRS #automation #design #research #tool support #towards #using #validation: [Research Paper] On the Use of Machine Learning Techniques Towards the Design of Cloud Based Automatic Code Clone Validation Tools (GM, JS, BR, CKR, KAS), pp. 155–164.
CIKM-2018-DingLX0S #optimisation #realtime: Optimizing Boiler Control in Real-Time with Machine Learning for Sustainability (YD, JL, JX, MJ0, YS), pp. 2147–2154.
ICML-2018-BollapragadaMNS: A Progressive Batching L-BFGS Method for Machine Learning (RB, DM, JN, HJMS, PTPT), pp. 619–628.
ICML-2018-DamaskinosMGPT: Asynchronous Byzantine Machine Learning (the case of SGD) (GD, EMEM, RG, RP, MT), pp. 1153–1162.
ICML-2018-KallusZ: Residual Unfairness in Fair Machine Learning from Prejudiced Data (NK, AZ), pp. 2444–2453.
ICML-2018-LiuDRSH: Delayed Impact of Fair Machine Learning (LTL, SD, ER, MS, MH), pp. 3156–3164.
ICML-2018-ZadikMS #orthogonal: Orthogonal Machine Learning: Power and Limitations (IZ, LWM, VS), pp. 5723–5731.
KDD-2018-AckermannWUNRLB #framework #modelling #policy: Deploying Machine Learning Models for Public Policy: A Framework (KA, JW, ADU, HN, ANR, SJL, JB, MD, CC, LH, RG), pp. 15–22.
KDD-2018-BeeckMSVD #predict: Fatigue Prediction in Outdoor Runners Via Machine Learning and Sensor Fusion (TODB, WM, KS0, BV, JD), pp. 606–615.
KDD-2018-Fan #approach: The Pinterest Approach to Machine Learning (LF), p. 2870.
KDD-2018-KumarRBVWKEFMZG #using: Using Machine Learning to Assess the Risk of and Prevent Water Main Breaks (AK, SAAR, BB, RAV, KHW, CK, SE, AF, AM, JZ, RG), pp. 472–480.
KDD-2018-RouxPMVF #approach #detection #using: Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach (DdR, BP, AM, MDPV, CF), pp. 215–222.
KDD-2018-StaarDAB #corpus #documentation #framework #platform #scalability: Corpus Conversion Service: A Machine Learning Platform to Ingest Documents at Scale (PWJS, MD, CA, CB), pp. 774–782.
KDD-2018-Xing #algorithm #co-evolution #design #named: SysML: On System and Algorithm Co-design for Practical Machine Learning (EPX), p. 2880.
ESEC-FSE-2018-HuZY #named #reuse #robust #testing #user interface #using: AppFlow: using machine learning to synthesize robust, reusable UI tests (GH, LZ, JY), pp. 269–282.
CC-2018-Shen #compilation: Rethinking compilers in the rise of machine learning and AI (keynote) (XS), p. 1.
ICST-2018-KhosrowjerdiMR #approach #injection #testing: Virtualized-Fault Injection Testing: A Machine Learning Approach (HK, KM, AR), pp. 297–308.
ECSA-2017-BhatSBHM #approach #automation #design: Automatic Extraction of Design Decisions from Issue Management Systems: A Machine Learning Based Approach (MB, KS, AB, UH, FM), pp. 138–154.
EDM-2017-BalyanMM #approach #comprehension #natural language: Combining Machine Learning and Natural Language Processing Approach to Assess Literary Text Comprehension (RB, KSM, DSM).
ICSME-2017-WangWW17a #recognition #semantics: Semantics-Aware Machine Learning for Function Recognition in Binary Code (SW0, PW0, DW), pp. 388–398.
AIIDE-2017-SnodgrassSO #generative: Studying the Effects of Training Data on Machine Learning-Based Procedural Content Generation (SS, AS, SO), pp. 122–128.
CIKM-2017-KimPP #modelling #performance: Machine Learning based Performance Modeling of Flash SSDs (JK, JP, SP), pp. 2135–2138.
CIKM-2017-LiHPG #detection #framework #named: DeMalC: A Feature-rich Machine Learning Framework for Malicious Call Detection (YL, DH, AP, ZG), pp. 1559–1567.
CIKM-2017-Rastogi: Machine Learning @ Amazon (RR), p. 1.
ICML-2017-KumarGV #internet #ram: Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things (AK, SG, MV), pp. 1935–1944.
ICML-2017-NguyenLST #named #novel #probability #problem #recursion #using: SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient (LMN, JL, KS, MT), pp. 2613–2621.
ICML-2017-SelsamLD: Developing Bug-Free Machine Learning Systems With Formal Mathematics (DS, PL, DLD), pp. 3047–3056.
KDD-2017-AndersonM #classification: Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity (BA, DAM), pp. 1723–1732.
KDD-2017-BaylorBCFFHHIJK #framework #named #platform: TFX: A TensorFlow-Based Production-Scale Machine Learning Platform (DB, EB, HTC, NF, CYF, ZH, SH, MI, VJ, LK0, CYK, LL, CM, ANM, NP, SR, SR0, SEW, MW, JW, XZ, MZ), pp. 1387–1395.
KDD-2017-Bloom #industrial: Industrial Machine Learning (JB), p. 13.
KDD-2017-ChengHHIMPRSSST #flexibility #framework: TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks (HTC, ZH, LH, MI, CM, IP, GR, DS, JS, DS, YT, PT, MW, CX, JX), pp. 1763–1771.
KDD-2017-KarpatneK #big data #challenge: Big Data in Climate: Opportunities and Challenges for Machine Learning (AK, VK), pp. 21–22.
KDD-2017-Pafka #question: Machine Learning Software in Practice: Quo Vadis? (SP), p. 25.
KDD-2017-RistovskiGHT #integration #optimisation #simulation: Dispatch with Confidence: Integration of Machine Learning, Optimization and Simulation for Open Pit Mines (KR, CG0, KH, HKT), pp. 1981–1989.
KDD-2017-SalehianHL #approach #crowdsourcing #scalability: Matching Restaurant Menus to Crowdsourced Food Data: A Scalable Machine Learning Approach (HS, PDH, CL), pp. 2001–2009.
KDD-2017-SharmaSKS #problem: The Fake vs Real Goods Problem: Microscopy and Machine Learning to the Rescue (AS, VS, VK, LS), pp. 2011–2019.
MoDELS-2017-HartmannMFT #domain model #evolution #integration #modelling: The Next Evolution of MDE: A Seamless Integration of Machine Learning into Domain Modeling (TH, AM, FF, YLT), p. 180.
ASE-2017-GodefroidPS #fuzzing: Learn&Fuzz: machine learning for input fuzzing (PG, HP, RS), pp. 50–59.
ESEC-FSE-2017-MaAXLZLZ #algorithm #graph #named: LAMP: data provenance for graph based machine learning algorithms through derivative computation (SM, YA, ZX, WCL, JZ, YL, XZ0), pp. 786–797.
ICSE-2017-VendomeVBPGP #detection #exception #open source: Machine learning-based detection of open source license exceptions (CV, MLV, GB, MDP, DMG, DP), pp. 118–129.
ICSME-2016-GopinathWHK #fault #using: Repairing Intricate Faults in Code Using Machine Learning and Path Exploration (DG, KW, JH, SK), pp. 453–457.
CIG-2016-DeboeverieRAVP #classification #game studies #gesture: Human gesture classification by brute-force machine learning for exergaming in physiotherapy (FD, SR, GA, PV, WP), pp. 1–7.
CIKM-2016-GuoXYHLLGC #data flow #process: Ease the Process of Machine Learning with Dataflow (TG, JX0, XY, JH, PL, ZL, JG, XC), pp. 2437–2440.
CIKM-2016-Najork #email #experience #using: Using Machine Learning to Improve the Email Experience (MN), p. 891.
ICML-2016-LibertyLS: Stratified Sampling Meets Machine Learning (EL, KJL, KS), pp. 2320–2329.
ICPR-2016-AginakoMRLS #approach #difference: Machine Learning approach to dissimilarity computation: Iris matching (NA, JMMO, IRR, EL, BS), pp. 170–175.
ICPR-2016-GordonL #modelling: Exposing and modeling underlying mechanisms in ALS with machine learning (JG0, BL), pp. 2168–2173.
ICPR-2016-KrompAWBDBGTAH #framework #image: Machine learning framework incorporating expert knowledge in tissue image annotation (FK, IA, TW, DB, HD, MB, TG, STM, PA, AH), pp. 343–348.
KDD-2016-Chayes #estimation #modelling #network: Graphons and Machine Learning: Modeling and Estimation of Sparse Massive Networks (JTC), p. 1.
KDD-2016-ChenH0 #web: Lifelong Machine Learning and Computer Reading the Web (ZC0, ERHJ, BL0), pp. 2117–2118.
KDD-2016-Downs #how: How Machine Learning has Finally Solved Wanamaker's Dilemma (OD), p. 405.
KDD-2016-HaarenSDF #using: Analyzing Volleyball Match Data from the 2014 World Championships Using Machine Learning Techniques (JVH, HBS, JD, PF), pp. 627–634.
KDD-2016-Mierswa #workflow: The Wisdom of Crowds: Best Practices for Data Prep & Machine Learning Derived from Millions of Data Science Workflows (IM), p. 411.
KDD-2016-RendleFSS #in the cloud #robust #scalability: Robust Large-Scale Machine Learning in the Cloud (SR, DF, EJS, BYS), pp. 1125–1134.
KDD-2016-SinghSA #independence #using: Question Independent Grading using Machine Learning: The Case of Computer Program Grading (GS, SS, VA), pp. 263–272.
KDD-2016-Srivastava #scalability #theory and practice: Large-Scale Machine Learning at Verizon: Theory and Applications (AS), p. 417.
KDD-2016-TaghaviLK #memory management #recommendation #using: Compute Job Memory Recommender System Using Machine Learning (TT, ML, YK), pp. 609–616.
ASE-2016-LiLQHBYCL #constraints #execution #symbolic computation #theorem proving: Symbolic execution of complex program driven by machine learning based constraint solving (XL, YL, HQ, YQH, LB, YY, XC, XL), pp. 554–559.
SLE-2016-ParrV #towards: Towards a universal code formatter through machine learning (TP, JJV), pp. 137–151.
DocEng-2015-SilvaFLCOSR #automation #documentation #summary: Automatic Text Document Summarization Based on Machine Learning (GPeS, RF, RDL, LdSC, HO, SJS, MR), pp. 191–194.
SIGMOD-2015-HuangBTRTR #scalability: Resource Elasticity for Large-Scale Machine Learning (BH, MB, YT, BR, ST, FRR), pp. 137–152.
SIGMOD-2015-ReABCJKR #database #question: Machine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype? (CR, DA, MB, MIC, MIJ, TK, RR), pp. 283–284.
TPDL-2015-Nunzio #education #geometry #naive bayes: Teaching Machine Learning: A Geometric View of Naïve Bayes (GMDN), pp. 343–346.
VLDB-2015-KumarJYNP #normalisation #optimisation: Demonstration of Santoku: Optimizing Machine Learning over Normalized Data (AK, MJ, BY, JFN, JMP), pp. 1864–1875.
EDM-2015-AlexandronZP #approach #student: Discovering the Pedagogical Resources that Assist Students to Answer Questions Correctly - A Machine Learning Approach (GA, QZ, DEP), pp. 520–523.
SIGITE-2015-YeraSLHSG: Work In Progress: Machine Learning In Robotics (GY, AS, HL, TH, CS, TG), p. 105.
CIG-2015-KarpovJM #behaviour: Evaluating team behaviors constructed with human-guided machine learning (IVK, LMJ, RM), pp. 292–298.
CHI-2015-AmershiCDLSS #analysis #named #performance #tool support: ModelTracker: Redesigning Performance Analysis Tools for Machine Learning (SA, MC, SMD, BL, PYS, JS), pp. 337–346.
CHI-2015-KatanGF #development #interactive #interface #people #using: Using Interactive Machine Learning to Support Interface Development Through Workshops with Disabled People (SK, MG, RF), pp. 251–254.
CSCW-2015-ChengB #classification #hybrid #named: Flock: Hybrid Crowd-Machine Learning Classifiers (JC, MSB), pp. 600–611.
ICML-2015-BlumH #contest #reliability: The Ladder: A Reliable Leaderboard for Machine Learning Competitions (AB, MH), pp. 1006–1014.
KDD-2015-Agarwal #scalability #statistics #web: Scaling Machine Learning and Statistics for Web Applications (DA), p. 1621.
KDD-2015-Athey #evaluation #policy: Machine Learning and Causal Inference for Policy Evaluation (SA), pp. 5–6.
KDD-2015-Durrant-Whyte: Data, Knowledge and Discovery: Machine Learning meets Natural Science (HDW), p. 7.
KDD-2015-Gomez-Rodriguez #modelling #network #probability #problem #research #social: Diffusion in Social and Information Networks: Research Problems, Probabilistic Models and Machine Learning Methods (MGR, LS), pp. 2315–2316.
KDD-2015-LakkarajuASMBGA #framework #identification #student: A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes (HL, EA, CS, DM, NB, RG, KLA), pp. 1909–1918.
KDD-2015-Pratt #predict #protocol #proving: Proof Protocol for a Machine Learning Technique Making Longitudinal Predictions in Dynamic Contexts (KBP), pp. 2049–2058.
KDD-2015-Schleier-Smith #agile #architecture #realtime: An Architecture for Agile Machine Learning in Real-Time Applications (JSS), pp. 2059–2068.
KDD-2015-SethiYRVR #classification #scalability #using: Scalable Machine Learning Approaches for Neighborhood Classification Using Very High Resolution Remote Sensing Imagery (MS, YY, AR, RRV, SR), pp. 2069–2078.
KDD-2015-ShashidharPA: Spoken English Grading: Machine Learning with Crowd Intelligence (VS, NP, VA), pp. 2089–2097.
KDD-2015-XingHDKWLZXKY #big data #distributed #framework #named #platform: Petuum: A New Platform for Distributed Machine Learning on Big Data (EPX, QH, WD, JKK, JW, SL, XZ, PX, AK, YY), pp. 1335–1344.
RecSys-2015-HuD #recommendation #scalability: Scalable Recommender Systems: Where Machine Learning Meets Search (SYDH, JD), pp. 365–366.
SEKE-2015-SaputriL #analysis #perspective: Are We Living in a Happy Country: An Analysis of National Happiness from Machine Learning Perspective (TRDS, SWL), pp. 174–177.
SAC-2015-FauconnierKR #approach #recognition #taxonomy: A supervised machine learning approach for taxonomic relation recognition through non-linear enumerative structures (JPF, MK, BR), pp. 423–425.
SAC-2015-NascimentoPM #algorithm #metaheuristic: A data quality-aware cloud service based on metaheuristic and machine learning provisioning algorithms (DCN, CESP, DGM), pp. 1696–1703.
ASPLOS-2015-LiuCLZZTFZC #named: PuDianNao: A Polyvalent Machine Learning Accelerator (DFL, TC, SL, JZ, SZ, OT, XF, XZ, YC), pp. 369–381.
CASE-2015-FarhanPWL #algorithm #predict #using: Predicting individual thermal comfort using machine learning algorithms (AAF, KRP, BW, PBL), pp. 708–713.
CASE-2015-SrinivasanBSSR #automation #modelling #network #using: Modelling time-varying delays in networked automation systems with heterogeneous networks using machine learning techniques (SS, FB, GS, BS, SR), pp. 362–368.
CASE-2015-SundarkumarRNG #api #detection #modelling #topic: Malware detection via API calls, topic models and machine learning (GGS, VR, IN, VG), pp. 1212–1217.
CASE-2015-SustoM #approach #multi #predict: Slow release drug dissolution profile prediction in pharmaceutical manufacturing: A multivariate and machine learning approach (GAS, SFM), pp. 1218–1223.
DAC-2015-VenkataramaniRL #classification #energy: Scalable-effort classifiers for energy-efficient machine learning (SV, AR, JL, MS), p. 6.
DATE-2015-ZhuM #linear #optimisation #programming #using: Optimizing dynamic trace signal selection using machine learning and linear programming (CSZ, SM), pp. 1289–1292.
HPCA-2015-WuGLJC #estimation #performance #using: GPGPU performance and power estimation using machine learning (GYW, JLG, AL, NJ, DC), pp. 564–576.
PPoPP-2015-AshariTBRCKS #kernel #on the #optimisation: On optimizing machine learning workloads via kernel fusion (AA, ST, MB, BR, KC, JK, PS), pp. 173–182.
DRR-2014-MaXA #algorithm #segmentation #video: A machine learning based lecture video segmentation and indexing algorithm (DM, BX, GA), p. ?–8.
SIGMOD-2014-CaiGLPVJ #algorithm #comparison #implementation #platform #scalability: A comparison of platforms for implementing and running very large scale machine learning algorithms (ZC, ZJG, SL, LLP, ZV, CMJ), pp. 1371–1382.
VLDB-2014-BoehmTRSTBV #hybrid #parallel #scalability: Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML (MB, ST, BR, PS, YT, DB, SV), pp. 553–564.
VLDB-2014-SunRYD #classification #crowdsourcing #named #scalability #using: Chimera: Large-Scale Classification using Machine Learning, Rules, and Crowdsourcing (CS, NR, FY, AD), pp. 1529–1540.
CHI-2014-KuleszaACFC #concept #evolution: Structured labeling for facilitating concept evolution in machine learning (TK, SA, RC, DF, DXC), pp. 3075–3084.
DUXU-DI-2014-GencerBZV #detection #mobile: Detection of Churned and Retained Users with Machine Learning Methods for Mobile Applications (MG, GB, ÖZ, TV), pp. 234–245.
ICEIS-v1-2014-ShakirIB #topic: Machine Learning Techniques for Topic Spotting (NS, EI, ISB), pp. 450–455.
ICML-c2-2014-HuS #multi #predict: Multi-period Trading Prediction Markets with Connections to Machine Learning (JH, AJS), pp. 1773–1781.
ICPR-2014-AodhaSBTGJ #interactive: Putting the Scientist in the Loop — Accelerating Scientific Progress with Interactive Machine Learning (OMA, VS, GJB, MT, MAG, KEJ), pp. 9–17.
ICPR-2014-BayramogluKEANKH #approach #detection #image #using: Detection of Tumor Cell Spheroids from Co-cultures Using Phase Contrast Images and Machine Learning Approach (NB, MK, LE, MA, MN, JK, JH), pp. 3345–3350.
ICPR-2014-MontagnerjH: A Machine Learning Based Method for Staff Removal (IdSM, RHJ, NSTH), pp. 3162–3167.
KDD-2014-Mullainathan #question #social: Bugbears or legitimate threats?: (social) scientists’ criticisms of machine learning? (SM), p. 4.
KDD-2014-Rudin #algorithm: Algorithms for interpretable machine learning (CR), p. 1519.
KDD-2014-SrikantA #programming #using: A system to grade computer programming skills using machine learning (SS, VA), pp. 1887–1896.
KDIR-2014-Bleiweiss #execution #using: SoC Processor Discovery for Program Execution Matching Using Unsupervised Machine Learning (AB), pp. 192–201.
SEKE-2014-SinghS #requirements #using: Software Requirement Prioritization using Machine Learning (DS, AS), pp. 701–704.
FSE-2014-Joseph #framework #interactive: Software programmer management: a machine learning and human computer interaction framework for optimal task assignment (HRJ), pp. 826–828.
ICSE-2014-LeeJP #behaviour #detection #memory management #modelling #using: Detecting memory leaks through introspective dynamic behavior modelling using machine learning (SL, CJ, SP), pp. 814–824.
CASE-2014-KernWGBM #estimation #using: COD and NH4-N estimation in the inflow of Wastewater Treatment Plants using Machine Learning Techniques (PK, CW, DG, MB, SFM), pp. 812–817.
CASE-2014-SustoWPZJOM #adaptation #flexibility #maintenance #predict: An adaptive machine learning decision system for flexible predictive maintenance (GAS, JW, SP, MZ, ABJ, PGO, SFM), pp. 806–811.
DAC-2014-AlbalawiLL #algorithm #classification #design #fixpoint #implementation #power management: Computer-Aided Design of Machine Learning Algorithm: Training Fixed-Point Classifier for On-Chip Low-Power Implementation (HA, YL, XL), p. 6.
OSDI-2014-LiAPSAJLSS #distributed #parametricity #scalability: Scaling Distributed Machine Learning with the Parameter Server (ML, DGA, JWP, AJS, AA, VJ, JL, EJS, BYS), pp. 583–598.
DocEng-2013-Esposito #documentation: Symbolic machine learning methods for historical document processing (FE), pp. 1–2.
ICDAR-2013-TuarobBMG #automation #detection #documentation #pseudo #using: Automatic Detection of Pseudocodes in Scholarly Documents Using Machine Learning (ST, SB, PM, CLG), pp. 738–742.
SIGMOD-2013-CondieMPW #big data: Machine learning for big data (TC, PM, NP, MW), pp. 939–942.
TPDL-2013-KlampflK #approach: An Unsupervised Machine Learning Approach to Body Text and Table of Contents Extraction from Digital Scientific Articles (SK, RK), pp. 144–155.
VLDB-2013-BergamaschiGILV #data-driven #database #keyword #named #relational #semantics: QUEST: A Keyword Search System for Relational Data based on Semantic and Machine Learning Techniques (SB, FG, MI, RTL, YV), pp. 1222–1225.
ICSM-2013-FontanaZMM #approach #detection #smell #towards: Code Smell Detection: Towards a Machine Learning-Based Approach (FAF, MZ, AM, MM), pp. 396–399.
ICSM-2013-OsmanCP #algorithm #analysis #diagrams: An Analysis of Machine Learning Algorithms for Condensing Reverse Engineered Class Diagrams (MHO, MRVC, PvdP), pp. 140–149.
ICSM-2013-SemenenkoDS #image #named #testing: Browserbite: Accurate Cross-Browser Testing via Machine Learning over Image Features (NS, MD, TS), pp. 528–531.
ICSM-2013-SorOTS #approach #detection #memory management #statistics #using: Improving Statistical Approach for Memory Leak Detection Using Machine Learning (VS, PO, TT, SNS), pp. 544–547.
CIG-2013-AlayedFN #behaviour #detection #online #using: Behavioral-based cheating detection in online first person shooters using machine learning techniques (HA, FF, CN), pp. 1–8.
DUXU-WM-2013-GencerBZV #framework #mobile #using: A New Framework for Increasing User Engagement in Mobile Applications Using Machine Learning Techniques (MG, GB, ÖZ, TV), pp. 651–659.
HCI-III-2013-StorzRMLE #analysis #detection #visualisation #workflow: Annotate. Train. Evaluate. A Unified Tool for the Analysis and Visualization of Workflows in Machine Learning Applied to Object Detection (MS, MR, RM, HL, ME), pp. 196–205.
CIKM-2013-Guestrin #scalability #usability: Usability in machine learning at scale with graphlab (CG), pp. 5–6.
ICML-c1-2013-MenonTGLK #framework #programming: A Machine Learning Framework for Programming by Example (AKM, OT, SG, BWL, AK), pp. 187–195.
ICML-c3-2013-GittensM #scalability: Revisiting the Nystrom method for improved large-scale machine learning (AG, MWM), pp. 567–575.
MLDM-2013-GopalakrishnaOLL #algorithm #metric: Relevance as a Metric for Evaluating Machine Learning Algorithms (AKG, TO, AL, JJL), pp. 195–208.
SEKE-2013-CarrerasZO: A Machine Learning Based File Archival Tool (RC, DZ, JO), pp. 73–76.
ICSE-2013-Jonsson #performance #scalability #using: Increasing anomaly handling efficiency in large organizations using applied machine learning (LJ), pp. 1361–1364.
SAC-2013-AkritidisB #algorithm #classification #research: A supervised machine learning classification algorithm for research articles (LA, PB), pp. 115–120.
SAC-2013-BerralGT #automation: Empowering automatic data-center management with machine learning (JLB, RG, JT), pp. 170–172.
CASE-2013-SharabianiDBCND #predict: Machine learning based prediction of warfarin optimal dosing for African American patients (AS, HD, AB, LC, EN, KD), pp. 623–628.
CC-2013-MooreC #automation #generative #policy #using: Automatic Generation of Program Affinity Policies Using Machine Learning (RWM, BRC), pp. 184–203.
CGO-2013-KulkarniCWS #automation #heuristic #using: Automatic construction of inlining heuristics using machine learning (SK, JC, CW, DS), p. 12.
DATE-2013-DeOrioLBB #debugging #detection: Machine learning-based anomaly detection for post-silicon bug diagnosis (AD, QL, MB, VB), pp. 491–496.
SIGMOD-2012-LinK #scalability #twitter: Large-scale machine learning at twitter (JL, AK), pp. 793–804.
TPDL-2012-RathodC: Machine Learning in Building a Collection of Computer Science Course Syllabi (NR, LNC), pp. 357–362.
VLDB-2012-LowGKBGH #distributed #framework #in the cloud: Distributed GraphLab: A Framework for Machine Learning in the Cloud (YL, JG, AK, DB, CG, JMH), pp. 716–727.
ITiCSE-2012-SperlingL #re-engineering #student: Integrating AI and machine learning in software engineering course for high school students (AS, DL), pp. 244–249.
ICPC-2012-Sajnani #approach #architecture #automation: Automatic software architecture recovery: A machine learning approach (HS), pp. 265–268.
AIIDE-2012-LeeBL #automation #recommendation: Sports Commentary Recommendation System (SCoReS): Machine Learning for Automated Narrative (GL, VB, EAL).
CHI-2012-AmershiFW #interactive #named #network #on-demand #social: Regroup: interactive machine learning for on-demand group creation in social networks (SA, JF, DSW), pp. 21–30.
ICML-2012-StorkeyMG: Isoelastic Agents and Wealth Updates in Machine Learning Markets (AJS, JM, KG), p. 133.
ICML-2012-Wagstaff #matter: Machine Learning that Matters (KW), p. 240.
ICPR-2012-VuralA #video: A machine learning system for human-in-the-loop video surveillance (UV, YSA), pp. 1092–1095.
KDD-2012-Lin #case study #data mining #experience #mining: Experiences and lessons in developing industry-strength machine learning and data mining software (CJL), p. 1176.
KDD-2012-ZhouKTX: Adversarial support vector machine learning (YZ, MK, BMT, BX), pp. 1059–1067.
KDIR-2012-Dagnino #approach #grid #information management #smarttech: Knowledge Discovery in the Smart Grid — A Machine Learning Approach (AD), pp. 366–369.
MLDM-2012-ChanguelL #independence #metadata #problem: Content Independent Metadata Production as a Machine Learning Problem (SC, NL), pp. 306–320.
MLDM-2012-TabatabaeiAKK #classification #internet: Machine Learning-Based Classification of Encrypted Internet Traffic (TST, MA, FK, MK), pp. 578–592.
SEKE-2012-DagninoSR #fault #using: Forecasting Fault Events in Power Distribution Grids Using Machine Learning (AD, KS, LR), pp. 458–463.
SEKE-2012-HaoWZ #classification #empirical: An Empirical Study of Execution-Data Classification Based on Machine Learning (DH, XW, LZ), pp. 283–288.
SIGIR-2012-LiX #web: Beyond bag-of-words: machine learning for query-document matching in web search (HL, JX), p. 1177.
SIGIR-2012-OzertemCDV #framework #query #ranking: Learning to suggest: a machine learning framework for ranking query suggestions (UO, OC, PD, EV), pp. 25–34.
OOPSLA-2012-KulkarniC #compilation #optimisation #problem #using: Mitigating the compiler optimization phase-ordering problem using machine learning (SK, JC), pp. 147–162.
ICSE-2012-Chioasca #automation #model transformation #requirements #using: Using machine learning to enhance automated requirements model transformation (EVC), pp. 1487–1490.
ICLP-2012-BlockeelBBCP #data mining #mining #modelling #problem: Modeling Machine Learning and Data Mining Problems with FO(·) (HB, BB, MB, BdC, SDP, MD, AL, JR, SV), pp. 14–25.
ICLP-2012-MarateaPR: Applying Machine Learning Techniques to ASP Solving (MM, LP, FR), pp. 37–48.
ICTSS-2012-StrugS #approach #mutation testing #testing: Machine Learning Approach in Mutation Testing (JS, BS), pp. 200–214.
SMT-2012-AzizWD #estimation #problem #smt: A Machine Learning Technique for Hardness Estimation of QFBV SMT Problems (MAA, AGW, NMD), pp. 57–66.
JCDL-2011-LearyRWWSM #automation #education: Automating open educational resources assessments: a machine learning generalization study (HL, MR, AEW, PGW, TS, JHM), pp. 283–286.
CIG-2011-AsheSK #data mining #mining #named: Keynotes: Data mining and machine learning applications in MMOs (GA, NRS, JHK).
CIG-2011-GalliLCL #approach #detection #framework: A cheating detection framework for Unreal Tournament III: A machine learning approach (LG, DL, LC, PLL), pp. 266–272.
CHI-2011-ChauKHF #interactive #named #network #scalability: Apolo: making sense of large network data by combining rich user interaction and machine learning (DHC, AK, JIH, CF), pp. 167–176.
HCI-MIIE-2011-KarthikP #adaptation #approach #classification #email: Adaptive Machine Learning Approach for Emotional Email Classification (KK, RP), pp. 552–558.
OCSC-2011-PujariK #approach #predict #recommendation: A Supervised Machine Learning Link Prediction Approach for Tag Recommendation (MP, RK), pp. 336–344.
ICEIS-J-2011-Li11f #analysis #approach #case study #type system #using: A Study on Noisy Typing Stream Analysis Using Machine Learning Approach (JL0), pp. 149–161.
CIKM-2011-QianHCZN #ambiguity: Combining machine learning and human judgment in author disambiguation (YnQ, YH, JC, QZ, ZN), pp. 1241–1246.
ECIR-2011-LeonardLZTCD #data fusion #information retrieval #metric: Applying Machine Learning Diversity Metrics to Data Fusion in Information Retrieval (DL, DL, LZ, FT, RWC, JD), pp. 695–698.
ICML-2011-SujeethLBRCWAOO #domain-specific language #named #parallel: OptiML: An Implicitly Parallel Domain-Specific Language for Machine Learning (AKS, HL, KJB, TR, HC, MW, ARA, MO, KO), pp. 609–616.
KDD-2011-ChauKHF #graph #interactive #named #scalability #visualisation: Apolo: interactive large graph sensemaking by combining machine learning and visualization (DHC, AK, JIH, CF), pp. 739–742.
KDD-2011-GhotingKPK #algorithm #data mining #implementation #mining #named #parallel #pipes and filters #tool support: NIMBLE: a toolkit for the implementation of parallel data mining and machine learning algorithms on mapreduce (AG, PK, EPDP, RK), pp. 334–342.
KDD-2011-VijayaraghavanK #data mining #mining #online: Applications of data mining and machine learning in online customer care (RV, PVK), p. 779.
MLDM-2011-TalbertHT #data mining #framework #mining: A Machine Learning and Data Mining Framework to Enable Evolutionary Improvement in Trauma Triage (DAT, MH, ST), pp. 348–361.
SEKE-2011-NoorianBD #classification #framework #testing #towards: Machine Learning-based Software Testing: Towards a Classification Framework (MN, EB, WD), pp. 225–229.
SIGIR-2011-LinLJY #approach #query #social: Social annotation in query expansion: a machine learning approach (YL, HL, SJ, ZY), pp. 405–414.
SIGIR-2011-ShiYGN #network #recommendation #scalability #social: A large scale machine learning system for recommending heterogeneous content in social networks (YS, DY, AG, SN), pp. 1337–1338.
SIGIR-2011-SiJ #information retrieval: Machine learning for information retrieval (LS, RJ), pp. 1293–1294.
SAS-2011-NoriR #program analysis: Program Analysis and Machine Learning: A Win-Win Deal (AVN, SKR), pp. 2–3.
ASE-2011-ChenHX #approach #evaluation #process: Software process evaluation: A machine learning approach (NC, SCHH, XX), pp. 333–342.
DAC-2011-GeQ #multi #using: Dynamic thermal management for multimedia applications using machine learning (YG, QQ), pp. 95–100.
ESOP-2011-BorgstromGGMG #semantics: Measure Transformer Semantics for Bayesian Machine Learning (JB, ADG, MG, JM, JVG), pp. 77–96.
EDM-2010-MontalvoBPNG #identification #student #using: Identifying Students’ Inquiry Planning Using Machine Learning (OM, RSJdB, MASP, AN, JDG), pp. 141–150.
ICML-2010-Apte #optimisation: The Role of Machine Learning in Business Optimization (CA), pp. 1–2.
ICML-2010-Raphael #music: Music Plus One and Machine Learning (CR), pp. 21–28.
ICML-2010-ShoebG #detection: Application of Machine Learning To Epileptic Seizure Detection (AHS, JVG), pp. 975–982.
ICPR-2010-Casarrubias-VargasPB #navigation #visual notation: EKF-SLAM and Machine Learning Techniques for Visual Robot Navigation (HCV, APB, EBC), pp. 396–399.
ICPR-2010-ShamiliBA #detection #distributed #mobile #using: Malware Detection on Mobile Devices Using Distributed Machine Learning (ASS, CB, TA), pp. 4348–4351.
KDD-2010-KhoslaCLCHL #approach #predict: An integrated machine learning approach to stroke prediction (AK, YC, CCYL, HKC, JH, HL), pp. 183–192.
KDIR-2010-CarulloB #analysis #mining #web: Machine Learning and Link Analysis for Web Content Mining (MC, EB), pp. 156–161.
KMIS-2010-FersiniMTAC #generative #semantics: Semantics and Machine Learning for Building the Next Generation of Judicial Court Management Systems (EF, EM, DT, FA, MC), pp. 51–60.
RecSys-2010-BenchettaraKR #approach #collaboration #predict #recommendation: A supervised machine learning link prediction approach for academic collaboration recommendation (NB, RK, CR), pp. 253–256.
SEKE-2010-KhoshgoftaarG #metric #novel #re-engineering #using: Software Engineering with Computational Intelligence and Machine Learning A Novel Software Metric Selection Technique Using the Area Under ROC Curves (TMK, KG), pp. 203–208.
SIGIR-2010-LeeCW #social: Uncovering social spammers: social honeypots + machine learning (KL, JC, SW), pp. 435–442.
ICSE-2010-Cleland-HuangCGE #approach #requirements: A machine learning approach for tracing regulatory codes to product specific requirements (JCH, AC, MG, JE), pp. 155–164.
DATE-2010-HuangSM #fault: Fault diagnosis of analog circuits based on machine learning (KH, HGDS, SM), pp. 1761–1766.
ICST-2010-SilvaJA #cost analysis #execution #symmetry #testing: Machine Learning Methods and Asymmetric Cost Function to Estimate Execution Effort of Software Testing (DGeS, MJ, BTdA), pp. 275–284.
JCDL-2009-LiC #approach #graph #kernel #predict #recommendation: Recommendation as link prediction: a graph kernel-based machine learning approach (XL, HC), pp. 213–216.
CHI-2009-TalbotLKT #classification #interactive #multi #named #visualisation: EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers (JT, BL, AK, DST), pp. 1283–1292.
HCI-VAD-2009-BaldirisFMG #adaptation: Adaptation Decisions and Profiles Exchange among Open Learning Management Systems Based on Agent Negotiations and Machine Learning Techniques (SB, RF, CM, SG), pp. 12–20.
HIMI-II-2009-AyodeleZK #approach #email #predict: Email Reply Prediction: A Machine Learning Approach (TA, SZ, RK), pp. 114–123.
ICEIS-DISI-2009-Mao #online: Machine Learning in Online Advertising (JM), p. 27.
CIKM-2009-SvoreB #approach #retrieval: A machine learning approach for improved BM25 retrieval (KMS, CJCB), pp. 1811–1814.
ICML-2009-BennettBC #information retrieval #summary #tutorial: Tutorial summary: Machine learning in IR: recent successes and new opportunities (PNB, MB, KCT), p. 17.
ICML-2009-BeygelzimerLZ #reduction #summary #tutorial: Tutorial summary: Reductions in machine learning (AB, JL, BZ), p. 12.
ICML-2009-SunJY #problem: A least squares formulation for a class of generalized eigenvalue problems in machine learning (LS, SJ, JY), pp. 977–984.
KDD-2009-JinHS #mining #named #novel #web: OpinionMiner: a novel machine learning system for web opinion mining and extraction (WJ, HHH, RKS), pp. 1195–1204.
MLDM-2009-SeredinKM #order #set: Selection of Subsets of Ordered Features in Machine Learning (OS, AK, VM), pp. 16–28.
SEKE-2009-AhsanFW #debugging #estimation #using: Program File Bug Fix Effort Estimation Using Machine Learning Methods for OSS (SNA, JF, FW), pp. 129–134.
SEKE-2009-AxelssonBFSK #code review #detection #fault #interactive #overview #visualisation: Detecting Defects with an Interactive Code Review Tool Based on Visualisation and Machine Learning (SA, DB, RF, DS, DK), pp. 412–417.
CGO-2009-LeatherBO #automation #compilation #generative #optimisation: Automatic Feature Generation for Machine Learning Based Optimizing Compilation (HL, EVB, MFPO), pp. 81–91.
DATE-2009-WangW: Machine learning-based volume diagnosis (SW, WW), pp. 902–905.
PPoPP-2009-WangO #approach #parallel: Mapping parallelism to multi-cores: a machine learning based approach (ZW, MFPO), pp. 75–84.
SAT-2009-HaimW #using: Restart Strategy Selection Using Machine Learning Techniques (SH, TW), pp. 312–325.
GDCSE-2008-WallaceRM #game studies: Integrating games and machine learning in the undergraduate computer science classroom (SAW, IR, ZM), pp. 56–60.
CHI-2008-PatelFLH #development #statistics: Investigating statistical machine learning as a tool for software development (KP, JF, JAL, BLH), pp. 667–676.
ICPR-2008-FerilliBBE #comprehension #documentation #incremental #layout: Incremental machine learning techniques for document layout understanding (SF, MB, TMAB, FE), pp. 1–4.
SEKE-2008-MurphyKHW #testing: Properties of Machine Learning Applications for Use in Metamorphic Testing (CM, GEK, LH, LW), pp. 867–872.
SEKE-2008-Zhang #re-engineering #research: Machine Learning and Value-based Software Engineering: a Research Agenda (DZ), pp. 285–290.
SAC-2008-SuKZG #classification #collaboration #using: Imputation-boosted collaborative filtering using machine learning classifiers (XS, TMK, XZ, RG), pp. 949–950.
DAC-2008-OzisikyilmazMC #design #performance #using: Efficient system design space exploration using machine learning techniques (BÖ, GM, ANC), pp. 966–969.
DATE-2008-KangK #design #framework #manycore #named #optimisation #performance: Magellan: A Search and Machine Learning-based Framework for Fast Multi-core Design Space Exploration and Optimization (SK, RK), pp. 1432–1437.
CIG-2007-FinkDA #behaviour #game studies #using: Extracting NPC behavior from computer games using computer vision and machine learning techniques (AF, JD, JA), pp. 24–31.
HIMI-MTT-2007-CornsML #approach #development #optimisation #using: Development of an Approach for Optimizing the Accuracy of Classifying Claims Narratives Using a Machine Learning Tool (TEXTMINER[4]) (HLC, HRM, MRL), pp. 411–416.
HIMI-MTT-2007-MullerKDCB #human-computer: Machine Learning and Applications for Brain-Computer Interfacing (KRM, MK, GD, GC, BB), pp. 705–714.
ECIR-2007-MoreauCS #automation #query #using: Automatic Morphological Query Expansion Using Analogy-Based Machine Learning (FM, VC, PS), pp. 222–233.
KDD-2007-RaoBFSON #detection #named: LungCAD: a clinically approved, machine learning system for lung cancer detection (RBR, JB, GF, MS, NO, DPN), pp. 1033–1037.
KDD-2007-YanL: Machine learning for stock selection (RJY, CXL), pp. 1038–1042.
MLDM-2007-ChristiansenD #approach #case study #evaluation #generative #testing: A Machine Learning Approach to Test Data Generation: A Case Study in Evaluation of Gene Finders (HC, CMD), pp. 742–755.
MLDM-2007-SadoddinG #case study #comparative #data mining #detection #mining: A Comparative Study of Unsupervised Machine Learning and Data Mining Techniques for Intrusion Detection (RS, AAG), pp. 404–418.
SEKE-2007-MurphyKA #approach #testing: An Approach to Software Testing of Machine Learning Applications (CM, GEK, MA), p. 167–?.
SAC-2007-YingboJJ #approach #workflow: A machine learning approach to semi-automating workflow staff assignment (YL, JW, JS), pp. 340–345.
LCTES-2007-AbouGhazalehFRXLCMM #cpu #scalability #using: Integrated CPU and l2 cache voltage scaling using machine learning (NA, APF, CR, RX, FL, BRC, DM, RGM), pp. 41–50.
ITiCSE-2006-RussellMN #education: Teaching AI through machine learning projects (IR, ZM, TWN), p. 323.
CIKM-2006-LuPLA #feature model #identification #query: Coupling feature selection and machine learning methods for navigational query identification (YL, FP, XL, NA), pp. 682–689.
ECIR-2006-VittautG #information retrieval #ranking: Machine Learning Ranking for Structured Information Retrieval (JNV, PG), pp. 338–349.
ICPR-v1-2006-Lampert #video: Machine Learning for Video Compression: Macroblock Mode Decision (CHL), pp. 936–940.
ICPR-v1-2006-LiHS #approach #bound #image: A Machine Learning Approach for Locating Boundaries of Liver Tumors in CT Images (YL, SH, KS), pp. 400–403.
ICPR-v2-2006-CamastraSV #algorithm #benchmark #challenge #metric #pattern matching #pattern recognition #recognition: Offline Cursive Character Challenge: a New Benchmark for Machine Learning and Pattern Recognition Algorithms. (FC, MS, AV), pp. 913–916.
CGO-2006-AgakovBCFFOTTW #optimisation #using: Using Machine Learning to Focus Iterative Optimization (FVA, EVB, JC, BF, GF, MFPO, JT, MT, CKIW), pp. 295–305.
ICDAR-2005-LiuCL #identification #image #using: Language Identification of Character Images Using Machine Learning Techniques (YHL, FC, CCL), pp. 630–634.
ICDAR-2005-SteinkrauSB #algorithm #using: Using GPUs for Machine Learning Algorithms (DS, PYS, IB), pp. 1115–1119.
JCDL-2005-HuLCMZ #automation #documentation #using: Automatic extraction of titles from general documents using machine learning (YH, HL, YC, DM, QZ), pp. 145–154.
ICSM-2005-FerencBFL #design pattern #mining: Design Pattern Mining Enhanced by Machine Learning (RF, ÁB, LJF, JL), pp. 295–304.
AIIDE-2005-SoutheyXHTB #analysis #automation: Semi-Automated Gameplay Analysis by Machine Learning (FS, GX, RCH, MT, JWB), pp. 123–128.
CIG-2005-ChisholmF #case study #game studies #using: A Study of Machine Learning using the Game of Fox and Geese (KC, DF).
CIG-2005-Pollack #named #research: Nannon: A Nano Backgammon for Machine Learning Research (JBP).
ICEIS-v2-2005-MashechkinPR #anti #approach #enterprise: Enterprise Anti-Spam Solution Based on Machine Learning Approach (IM, MP, AR), pp. 188–193.
CIKM-2005-CarinoJLWY #mining #web: Mining officially unrecognized side effects of drugs by combining web search and machine learning (CC, YJ, BL, PMW, CTY), pp. 365–372.
CIKM-2005-NottelmannS #information retrieval #probability: Information retrieval and machine learning for probabilistic schema matching (HN, US), pp. 295–296.
ICML-2005-IresonCCFKL #information management: Evaluating machine learning for information extraction (NI, FC, MEC, DF, NK, AL), pp. 345–352.
ICML-2005-ScholkopfSB #problem: Object correspondence as a machine learning problem (BS, FS, VB), pp. 776–783.
RE-2005-AvesaniBPS #requirements #scalability: Facing Scalability Issues in Requirements Prioritization with Machine Learning Techniques (PA, CB, AP, AS), pp. 297–306.
ICSE-2005-Fox #dependence #statistics: Addressing software dependability with statistical and machine learning techniques (AF), p. 8.
CGO-2005-Hind #architecture #virtual machine: Virtual Machine Learning: Thinking like a Computer Architect (MH), p. 11.
JCDL-2004-EfronEMZ #architecture #scalability: Machine learning for information architecture in a large governmental website (ME, JLE, GM, JZ), pp. 151–159.
ICML-2004-TsochantaridisHJA: Support vector machine learning for interdependent and structured output spaces (IT, TH, TJ, YA).
KDD-2004-Muslea #online #query: Machine learning for online query relaxation (IM), pp. 246–255.
SEKE-2004-AvesaniBPS #approach #process #requirements: Supporting the Requirements Prioritization Process. A Machine Learning approach (PA, CB, AP, AS), pp. 306–311.
SIGIR-2004-ZhangPZ #recognition #using: Focused named entity recognition using machine learning (LZ, YP, TZ), pp. 281–288.
ICSE-2004-BrunE #fault: Finding Latent Code Errors via Machine Learning over Program Executions (YB, MDE), pp. 480–490.
ICDAR-2003-MalerbaEACB #approach #documentation #layout: Correcting the Document Layout: A Machine Learning Approach (DM, FE, OA, MC, MB), p. 97–?.
ITiCSE-2003-GeorgiopoulosCWDGGKM #case study #experience: CRCD in machine learning at the University of Central Florida preliminary experiences (MG, JC, ASW, RFD, EG, AJG, MKK, MM), p. 249.
ECIR-2003-ShiEMSLLKO #approach: A Machine Learning Approach for the Curation of Biomedical Literature (MS, DSE, RM, LS, JYKL, HTL, SSK, CJO), pp. 597–604.
ICML-2003-Flach #comprehension #geometry #metric: The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics (PAF), pp. 194–201.
ICML-2003-OngS #kernel: Machine Learning with Hyperkernels (CSO, AJS), pp. 568–575.
KDD-2003-FradkinM #random: Experiments with random projections for machine learning (DF, DM), pp. 517–522.
MLDM-2003-Bunke #data mining #graph #mining #tool support: Graph-Based Tools for Data Mining and Machine Learning (HB), pp. 7–19.
MLDM-2003-PiwowarskiG #documentation #information retrieval: A Machine Learning Model for Information Retrieval with Structured Documents (BP, PG), pp. 425–438.
SEKE-2003-SpanoudakisGZ #approach #requirements #traceability: Revising Rules to Capture Requirements Traceability Relations: A Machine Learning Approach (GS, ASdG, AZ), pp. 570–577.
PLDI-2003-StephensonAMO #compilation #heuristic #optimisation: Meta optimization: improving compiler heuristics with machine learning (MS, SPA, MCM, UMO), pp. 77–90.
PPoPP-2003-Puppin #adaptation #convergence #scheduling #using: Adapting convergent scheduling using machine learning (DP), p. 1.
CAiSE-2002-BerlinM #database #feature model #using: Database Schema Matching Using Machine Learning with Feature Selection (JB, AM), pp. 452–466.
ICPR-v2-2002-Maloof #analysis #on the #statistics #testing: On Machine Learning, ROC Analysis, and Statistical Tests of Significance (MAM), pp. 204–207.
CAV-2002-ClarkeGKS #abstraction #satisfiability #using: SAT Based Abstraction-Refinement Using ILP and Machine Learning Techniques (EMC, AG, JHK, OS), pp. 265–279.
ICDAR-2001-NatteeN #classification #comprehension #documentation #geometry #online #using: Geometric Method for Document Understanding and Classification Using On-line Machine Learning (CN, MN), pp. 602–606.
ICEIS-v1-2001-DiazTO #using: A Knowledge-Acquisition Methodology for a Blast Furnace Expert System Using Machine Learning Techniques (ED, JT, FO), pp. 336–339.
ICEIS-v1-2001-SierraRLG #analysis #image #mobile #order #recognition: Machine Learning Approaches for Image Analysis: Recognition of Hand Orders by a Mobile Robot (BS, IR, EL, UG), pp. 330–335.
ICML-2001-DomingosH #algorithm #clustering #scalability: A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering (PMD, GH), pp. 106–113.
ICML-2000-Hall #feature model: Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning (MAH), pp. 359–366.
ICML-2000-KomarekM #adaptation #performance #scalability #set: A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets (PK, AWM), pp. 495–502.
ICML-2000-Langley: Crafting Papers on Machine Learning (PL), pp. 1207–1216.
ICML-2000-MollPB #problem: Machine Learning for Subproblem Selection (RM, TJP, AGB), pp. 615–622.
ICML-2000-SmolaS #approximate #matrix: Sparse Greedy Matrix Approximation for Machine Learning (AJS, BS), pp. 911–918.
SIGIR-2000-ChuangY #approach #summary: Extracting sentence segments for text summarization: a machine learning approach (WTC, JY), pp. 152–159.
SIGIR-2000-PetasisCVPKS #adaptation #automation #probability: Automatic adaptation of proper noun dictionaries through cooperation of machine learning and probabilistic methods (GP, AC, PV, GP, VK, CDS), pp. 128–135.
ICML-1998-LiquiereS #graph: Structural Machine Learning with Galois Lattice and Graphs (ML, JS), pp. 305–313.
ASE-1998-MaoSL #case study #reuse #usability #using #verification: Reusability Hypothesis Verification using Machine Learning Techniques: A Case Study (YM, HAS, HL), pp. 84–93.
ECDL-1997-SemeraroEMFF #library #online: Machine Learning + On-line Libraries = IDL (GS, FE, DM, NF, SF), pp. 195–214.
ICDAR-1997-AminKS #recognition: Hand Printed Chinese Character Recognition via Machine Learning (AA, SGK, CS), pp. 190–194.
ICDAR-1997-EspositoMSAG #library #semantics: Information Capture and Semantic Indexing of Digital Libraries through Machine Learning Techniques (FE, DM, GS, CDA, GdG), pp. 722–727.
PODS-1997-GunopulosKMT #data mining #mining: Data mining, Hypergraph Transversals, and Machine Learning (DG, RK, HM, HT), pp. 209–216.
HCI-SEC-1997-Moustakis #human-computer #people #question: Do People in HCI Use Machine Learning? (VM), pp. 95–98.
HCI-SEC-1997-Nguifo #interactive: An Interactive Environment for Dynamic Control of Machine Learning Systems (EMN), pp. 31–34.
HCI-SEC-1997-Pohl #modelling #named: LaboUr — Machine Learning for User Modeling (WP), pp. 27–30.
ICML-1997-ZupanBBD #composition: Machine Learning by Function Decomposition (BZ, MB, IB, JD), pp. 421–429.
KDD-1997-BergstenSS #analysis #data mining #mining: Applying Data Mining and Machine Learning Techniques to Submarine Intelligence Analysis (UB, JS, PS), pp. 127–130.
KDD-1997-KramerPH #mining: Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail (SK, BP, CH), pp. 223–226.
ICML-1996-Mannila #data mining #mining: Data Mining and Machine Learning (HM), p. 555.
ICPR-1996-DemsarS #image #using: Using machine learning for content-based image retrieving (JD, FS), pp. 138–142.
KDD-1996-FawcettP #data mining #effectiveness #mining #profiling: Combining Data Mining and Machine Learning for Effective User Profiling (TF, FJP), pp. 8–13.
KDD-1996-LakshminarayanHGS #using: Imputation of Missing Data Using Machine Learning Techniques (KL, SAH, RPG, TS), pp. 140–145.
ICDAR-v2-1995-DengelD #approach #classification #clustering #documentation: Clustering and classification of document structure-a machine learning approach (AD, FD), pp. 587–591.
ICDAR-v2-1995-ZiinoAS #recognition #using: Recognition of hand printed Latin characters using machine learning (DZ, AA, CS), pp. 1098–1102.
ICML-1995-Croft #information retrieval: Machine Learning and Information Retrieval (WBC), p. 587.
ICML-1995-SquiresS #automation #recognition: Automatic Speaker Recognition: An Application of Machine Learning (BS, CS), pp. 515–521.
KDD-1995-ChanS #scalability: Learning Arbiter and Combiner Trees from Partitioned Data for Scaling Machine Learning (PKC, SJS), pp. 39–44.
SAC-1995-StearnsC #concept #rule-based: Rule-based machine learning of spatial data concepts (SS, DCSC), pp. 242–247.
DL-1994-FayyadS #analysis #approach #automation #image #library: The Automated Analysis, Cataloging, and Searching of Digital Image Libraries: A Machine Learning Approach (UMF, PS), pp. 225–249.
ICML-1994-DruckerCJCV #algorithm: Boosting and Other Machine Learning Algorithms (HD, CC, LDJ, YL, VV), pp. 53–61.
ICML-1994-Pereira #bias #natural language #problem: Frequencies vs. Biases: Machine Learning Problems in Natural Language Processing — Abstract (FCNP), p. 380.
KDD-1994-AronisP #induction #relational: Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning (JMA, FJP), pp. 347–358.
KDD-1994-SasisekharanSW #maintenance #network #using: Proactive Network Maintenance Using Machine Learning (RS, VS, SMW), pp. 453–462.
KBSE-1994-MintonW #source code #using: Using Machine Learning to Synthesize Search Programs (SM, SRW), pp. 31–38.
ICML-1993-FayyadWD #automation #named #scalability: SKICAT: A Machine Learning System for Automated Cataloging of Large Scale Sky Surveys (UMF, NW, SGD), pp. 112–119.
SEKE-1993-EspositoMS #information management #refinement: Machine Learning Techniques for Knowledge Acquisition and Refinement (FE, DM, GS), pp. 319–323.
SEKE-1993-WillisP #program transformation #reuse: Machine Learning for Program Transformations in Software Reuse (CPW, DJP), pp. 275–277.
CAiSE-1992-FouqueV #analysis #approach: Building a Tool for Software Code Analysis: A Machine Learning Approach (GF, CV), pp. 278–289.
ML-1991-ChienWDDFGL #automation: Machine Learning in Engineering Automation (SAC, BLW, TGD, RJD, BF, JG, SCYL), pp. 577–580.
ML-1991-ORorkeMABC #evaluation: Machine Learning for Nondestructive Evaluation (PO, SM, MA, WB, DCSC), pp. 620–624.
ML-1991-Thompson #approach #information retrieval: Machine Learning in the Combination of Expert Opinion Approach to IR (PT), pp. 270–274.
KBSE-1991-HarandiL #design #perspective: Acquiring Software Design Schemas: A Machine Learning Perspective (MTH, HYL), pp. 188–197.
ML-1990-Holder #problem: The General Utility Problem in Machine Learning (LBH), pp. 402–410.
SIGIR-1990-HalinCK #image #retrieval: Machine Learning and Vectorial Matching for an Image Retrieval Model: EXPRIM and the System RIVAGE (GH, MC, PK), pp. 99–114.
ML-1989-MuggletonBMM #comparison: An Experimental Comparison of Human and Machine Learning Formalisms (SM, MB, JHM, DM), pp. 113–118.
ML-1989-Subramanian: Representational Issues in Machine Learning (DS), pp. 426–429.
NACLP-1989-MarkovitchS #approach #automation: Automatic Ordering of Subgoals — A Machine Learning Approach (SM, PDS), pp. 224–240.
SIGIR-1986-WongZ #approach #information retrieval: A Machine Learning Approach to Information Retrieval (SKMW, WZ), pp. 228–233.